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Repository: DeepGraphLearning/NBFNet
Branch: master
Commit: eb49ff2770d1
Files: 23
Total size: 620.1 KB
Directory structure:
gitextract_671zkorr/
├── .gitignore
├── LICENSE
├── README.md
├── config/
│ ├── homogeneous_graph/
│ │ ├── citeseer.yaml
│ │ ├── cora.yaml
│ │ └── pubmed.yaml
│ ├── inductive/
│ │ ├── fb15k237.yaml
│ │ └── wn18rr.yaml
│ └── knowledge_graph/
│ ├── fb15k237.yaml
│ ├── fb15k237_visualize.yaml
│ ├── ogbl-biokg.yaml
│ ├── ogbl-biokg_test.yaml
│ └── wn18rr.yaml
├── data/
│ └── fb15k237_entity.txt
├── nbfnet/
│ ├── __init__.py
│ ├── dataset.py
│ ├── layer.py
│ ├── model.py
│ ├── task.py
│ └── util.py
├── requirements.txt
└── script/
├── run.py
└── visualize.py
================================================
FILE CONTENTS
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FILE: .gitignore
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# PyCharm
/.idea
# VS Code
/.vscode
# Python
__pycache__
*.pyc
# macOS
.DS_Store
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2021 MilaGraph
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
# NBFNet: Neural Bellman-Ford Networks #
This is the official codebase of the paper
[Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction][paper]
[Zhaocheng Zhu](https://kiddozhu.github.io),
[Zuobai Zhang](https://oxer11.github.io),
[Louis-Pascal Xhonneux](https://github.com/lpxhonneux),
[Jian Tang](https://jian-tang.com)
[paper]: https://arxiv.org/pdf/2106.06935.pdf
A PyG re-implementation of NBFNet can be found [here](https://github.com/KiddoZhu/NBFNet-PyG).
## Overview ##
NBFNet is a graph neural network framework inspired by traditional path-based
methods. It enjoys the advantages of both traditional path-based methods and modern
graph neural networks, including **generalization in the inductive setting**,
**interpretability**, **high model capacity** and **scalability**. NBFNet can be
applied to solve link prediction on both homogeneous graphs and knowledge graphs.
This codebase is based on PyTorch and [TorchDrug]. It supports training and inference
with multiple GPUs or multiple machines.
[TorchDrug]: https://github.com/DeepGraphLearning/torchdrug
## Installation ##
You may install the dependencies via either conda or pip. Generally, NBFNet works
with Python 3.7/3.8 and PyTorch version >= 1.8.0.
### From Conda ###
```bash
conda install torchdrug pytorch=1.8.2 cudatoolkit=11.1 -c milagraph -c pytorch-lts -c pyg -c conda-forge
conda install ogb easydict pyyaml -c conda-forge
```
### From Pip ###
```bash
pip install torch==1.8.2+cu111 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
pip install torchdrug
pip install ogb easydict pyyaml
```
## Reproduction ##
To reproduce the results of NBFNet, use the following command. Alternatively, you
may use `--gpus null` to run NBFNet on a CPU. All the datasets will be automatically
downloaded in the code.
```bash
python script/run.py -c config/inductive/wn18rr.yaml --gpus [0] --version v1
```
We provide the hyperparameters for each experiment in configuration files.
All the configuration files can be found in `config/*/*.yaml`.
For experiments on inductive relation prediction, you need to additionally specify
the split version with `--version v1`.
To run NBFNet with multiple GPUs or multiple machines, use the following commands
```bash
python -m torch.distributed.launch --nproc_per_node=4 script/run.py -c config/inductive/wn18rr.yaml --gpus [0,1,2,3]
```
```bash
python -m torch.distributed.launch --nnodes=4 --nproc_per_node=4 script/run.py -c config/inductive/wn18rr.yaml --gpus [0,1,2,3,0,1,2,3,0,1,2,3,0,1,2,3]
```
### Visualize Interpretations on FB15k-237 ###
Once you have models trained on FB15k237, you can visualize the path interpretations
with the following line. Please replace the checkpoint with your own path.
```bash
python script/visualize.py -c config/knowledge_graph/fb15k237_visualize.yaml --checkpoint /path/to/nbfnet/experiment/model_epoch_20.pth
```
### Evaluate ogbl-biokg ###
Due to the large size of ogbl-biokg, we only evaluate on a small portion of the
validation set during training. The following line evaluates a model on the full
validation / test sets of ogbl-biokg. Please replace the checkpoint with your own
path.
```bash
python script/run.py -c config/knowledge_graph/ogbl-biokg_test.yaml --checkpoint /path/to/nbfnet/experiment/model_epoch_10.pth
```
## Results ##
Here are the results of NBFNet on standard benchmark datasets. All the results are
obtained with 4 V100 GPUs (32GB). Note results may be slightly different if the
model is trained with 1 GPU and/or a smaller batch size.
### Knowledge Graph Completion ###
<table>
<tr>
<th>Dataset</th>
<th>MR</th>
<th>MRR</th>
<th>HITS@1</th>
<th>HITS@3</th>
<th>HITS@10</th>
</tr>
<tr>
<th>FB15k-237</th>
<td>114</td>
<td>0.415</td>
<td>0.321</td>
<td>0.454</td>
<td>0.599</td>
</tr>
<tr>
<th>WN18RR</th>
<td>636</td>
<td>0.551</td>
<td>0.497</td>
<td>0.573</td>
<td>0.666</td>
</tr>
<tr>
<th>ogbl-biokg</th>
<td>-</td>
<td>0.829</td>
<td>0.768</td>
<td>0.870</td>
<td>0.946</td>
</tr>
</table>
### Homogeneous Graph Link Prediction ###
<table>
<tr>
<th>Dataset</th>
<th>AUROC</th>
<th>AP</th>
</tr>
<tr>
<th>Cora</th>
<td>0.956</td>
<td>0.962</td>
</tr>
<tr>
<th>CiteSeer</th>
<td>0.923</td>
<td>0.936</td>
</tr>
<tr>
<th>PubMed</th>
<td>0.983</td>
<td>0.982</td>
</tr>
</table>
### Inductive Relation Prediction ###
<table>
<tr>
<th rowspan="2">Dataset</th>
<th colspan="4">HITS@10 (50 sample)</th>
</tr>
<tr>
<th>v1</th>
<th>v2</th>
<th>v3</th>
<th>v4</th>
</tr>
<tr>
<th>FB15k-237</th>
<td>0.834</td>
<td>0.949</td>
<td>0.951</td>
<td>0.960</td>
</tr>
<tr>
<th>WN18RR</th>
<td>0.948</td>
<td>0.905</td>
<td>0.893</td>
<td>0.890</td>
</tr>
</table>
Frequently Asked Questions
--------------------------
1. **The code is stuck at the beginning of epoch 0.**
This is probably because the JIT cache is broken.
Try `rm -r ~/.cache/torch_extensions/*` and run the code again.
Citation
--------
If you find this codebase useful in your research, please cite the following paper.
```bibtex
@article{zhu2021neural,
title={Neural bellman-ford networks: A general graph neural network framework for link prediction},
author={Zhu, Zhaocheng and Zhang, Zuobai and Xhonneux, Louis-Pascal and Tang, Jian},
journal={Advances in Neural Information Processing Systems},
volume={34},
year={2021}
}
```
================================================
FILE: config/homogeneous_graph/citeseer.yaml
================================================
output_dir: ~/experiments/
dataset:
class: CiteSeerLinkPrediction
path: ~/datasets/homogeneous_graphs/
task:
class: LinkPrediction
model:
class: NBFNet
input_dim: 32
hidden_dims: [32, 32, 32, 32, 32, 32]
message_func: distmult
aggregate_func: pna
short_cut: yes
layer_norm: yes
dependent: no
remove_one_hop: yes
symmetric: yes
criterion: bce
num_negative: 1
strict_negative: yes
optimizer:
class: Adam
lr: 5.0e-3
engine:
gpus: {{ gpus }}
batch_size: 64
train:
num_epoch: 20
metric: auroc
================================================
FILE: config/homogeneous_graph/cora.yaml
================================================
output_dir: ~/experiments/
dataset:
class: CoraLinkPrediction
path: ~/datasets/homogeneous_graphs/
task:
class: LinkPrediction
model:
class: NBFNet
input_dim: 32
hidden_dims: [32, 32, 32, 32, 32, 32]
message_func: distmult
aggregate_func: pna
short_cut: yes
layer_norm: yes
dependent: no
remove_one_hop: yes
symmetric: yes
criterion: bce
num_negative: 1
strict_negative: yes
optimizer:
class: Adam
lr: 5.0e-3
engine:
gpus: {{ gpus }}
batch_size: 64
train:
num_epoch: 20
metric: auroc
================================================
FILE: config/homogeneous_graph/pubmed.yaml
================================================
output_dir: ~/experiments/
dataset:
class: PubMedLinkPrediction
path: ~/datasets/homogeneous_graphs/
task:
class: LinkPrediction
model:
class: NBFNet
input_dim: 32
hidden_dims: [32, 32, 32, 32, 32, 32]
message_func: distmult
aggregate_func: pna
short_cut: yes
layer_norm: yes
dependent: no
remove_one_hop: yes
symmetric: yes
criterion: bce
num_negative: 1
strict_negative: yes
optimizer:
class: Adam
lr: 5.0e-3
engine:
gpus: {{ gpus }}
batch_size: 16
train:
num_epoch: 10
metric: auroc
================================================
FILE: config/inductive/fb15k237.yaml
================================================
output_dir: ~/experiments/
dataset:
class: FB15k237Inductive
path: ~/datasets/knowledge_graphs/
version: {{ version }}
task:
class: InductiveKnowledgeGraphCompletion
model:
class: NBFNet
input_dim: 32
hidden_dims: [32, 32, 32, 32, 32, 32]
message_func: distmult
aggregate_func: pna
short_cut: yes
layer_norm: yes
dependent: yes
remove_one_hop: yes
criterion: bce
num_negative: 32
strict_negative: yes
adversarial_temperature: 0.5
sample_weight: no
full_batch_eval: yes
optimizer:
class: Adam
lr: 5.0e-3
engine:
gpus: {{ gpus }}
batch_size: 64
train:
num_epoch: 20
metric: mrr
================================================
FILE: config/inductive/wn18rr.yaml
================================================
output_dir: ~/experiments/
dataset:
class: WN18RRInductive
path: ~/datasets/knowledge_graphs/
version: {{ version }}
task:
class: InductiveKnowledgeGraphCompletion
model:
class: NBFNet
input_dim: 32
hidden_dims: [32, 32, 32, 32, 32, 32]
message_func: distmult
aggregate_func: pna
short_cut: yes
layer_norm: yes
dependent: no
criterion: bce
num_negative: 32
strict_negative: yes
adversarial_temperature: 1
sample_weight: no
full_batch_eval: yes
optimizer:
class: Adam
lr: 5.0e-3
engine:
gpus: {{ gpus }}
batch_size: 32
train:
num_epoch: 20
metric: mrr
================================================
FILE: config/knowledge_graph/fb15k237.yaml
================================================
output_dir: ~/experiments/
dataset:
class: FB15k237
path: ~/datasets/knowledge_graphs/
task:
class: KnowledgeGraphCompletion
model:
class: NBFNet
input_dim: 32
hidden_dims: [32, 32, 32, 32, 32, 32]
message_func: distmult
aggregate_func: pna
short_cut: yes
layer_norm: yes
dependent: yes
remove_one_hop: yes
criterion: bce
num_negative: 32
strict_negative: yes
adversarial_temperature: 0.5
sample_weight: no
full_batch_eval: yes
optimizer:
class: Adam
lr: 5.0e-3
engine:
gpus: {{ gpus }}
batch_size: 64
train:
num_epoch: 20
metric: mrr
================================================
FILE: config/knowledge_graph/fb15k237_visualize.yaml
================================================
output_dir: ~/experiments/
dataset:
class: FB15k237
path: ~/datasets/knowledge_graphs/
task:
class: KnowledgeGraphCompletion
model:
class: NBFNet
input_dim: 32
hidden_dims: [32, 32, 32, 32, 32, 32]
message_func: distmult
aggregate_func: pna
short_cut: yes
layer_norm: yes
dependent: yes
remove_one_hop: yes
criterion: bce
num_negative: 32
strict_negative: yes
adversarial_temperature: 0.5
sample_weight: no
full_batch_eval: yes
optimizer:
class: Adam
lr: 5.0e-3
engine:
gpus: [0]
batch_size: 64
checkpoint: {{ checkpoint }}
metric: mrr
================================================
FILE: config/knowledge_graph/ogbl-biokg.yaml
================================================
output_dir: ~/experiments/
dataset:
class: OGBLBioKG
path: ~/datasets/knowledge_graphs/
task:
class: KnowledgeGraphCompletionOGB
model:
class: NBFNet
input_dim: 32
hidden_dims: [32, 32, 32, 32, 32, 32]
message_func: distmult
aggregate_func: pna
short_cut: yes
layer_norm: yes
dependent: yes
remove_one_hop: yes
criterion: bce
evaluator:
class: Evaluator
name: ogbl-biokg
num_negative: 32
strict_negative: yes
heterogeneous_negative: yes
adversarial_temperature: 0.5
sample_weight: no
optimizer:
class: Adam
lr: 2.0e-4
engine:
gpus: {{ gpus }}
batch_size: 8
log_interval: 10
train:
num_epoch: 10
batch_per_epoch: 200
metric: mrr
fast_test: 1000
================================================
FILE: config/knowledge_graph/ogbl-biokg_test.yaml
================================================
output_dir: ~/experiments/
dataset:
class: OGBLBioKG
path: ~/datasets/knowledge_graphs/
task:
class: KnowledgeGraphCompletionOGB
model:
class: NBFNet
input_dim: 32
hidden_dims: [32, 32, 32, 32, 32, 32]
message_func: distmult
aggregate_func: pna
short_cut: yes
layer_norm: yes
dependent: yes
remove_one_hop: yes
criterion: bce
evaluator:
class: Evaluator
name: ogbl-biokg
num_negative: 32
strict_negative: yes
heterogeneous_negative: yes
adversarial_temperature: 0.5
sample_weight: no
optimizer:
class: Adam
lr: 2.0e-4
engine:
gpus: {{ gpus }}
batch_size: 32
log_interval: 10
train:
num_epoch: 0
batch_per_epoch: 200
checkpoint: {{ checkpoint }}
metric: mrr
================================================
FILE: config/knowledge_graph/wn18rr.yaml
================================================
output_dir: ~/experiments/
dataset:
class: WN18RR
path: ~/datasets/knowledge_graphs/
task:
class: KnowledgeGraphCompletion
model:
class: NBFNet
input_dim: 32
hidden_dims: [32, 32, 32, 32, 32, 32]
message_func: distmult
aggregate_func: pna
short_cut: yes
layer_norm: yes
dependent: no
criterion: bce
num_negative: 32
strict_negative: yes
adversarial_temperature: 1
sample_weight: no
full_batch_eval: yes
optimizer:
class: Adam
lr: 5.0e-3
engine:
gpus: {{ gpus }}
batch_size: 32
train:
num_epoch: 20
metric: mrr
================================================
FILE: data/fb15k237_entity.txt
================================================
/m/027rn languages of dominican republic (Q786)
/m/06cx9 republic (government) (Q7270)
/m/017dcd rangers in reverse (Q513809)
/m/06v8s0 elyse floyd (Q2642437)
/m/07s9rl0 drama movie (Q130232)
/m/0170z3 danny vinyard (Q208572)
/m/01sl1q michelle rodríguez (Q184219)
/m/044mz_ naveen andrews (Q208374)
/m/0cnk2q australia national soccer team (Q268208)
/m/02nzb8 right winger (football) (Q193592)
/m/04nrcg mdv (Q387312)
/m/02sdk9v outside forward (Q280658)
/m/07nznf bryan singer/comments (Q220751)
/m/014lc_ star trek 11 (cancelled film) (Q388319)
/m/02qyp19 bafta award for best original screenplay 1983–1999 (Q41375)
/m/02d413 philadelphia (film) (Q204057)
/m/0q9kd daniel michael devito jr. (Q26806)
/m/0184jc guy pearce (Q223745)
/m/03q5t harpischord (Q81982)
/m/07y_7 violin tuning (Q8355)
/m/0gqng academy award for best foreign language film (Q105304)
/m/073hkh 61th academy awards (Q924339)
/m/0b76d_m anonymous (film) (Q313819)
/m/029j_ dual layer (Q5294)
/m/014_x2 city of angels (film) (Q168010)
/m/012ljv gabriel yared (Q502957)
/m/0ds35l9 bridesmaids (2011 film) (Q837264)
/m/05r4w republica portuguesa (Q45)
/m/015qsq serpico (film) (Q657079)
/m/02bjrlw history of the italian language (Q652)
/m/04bdxl catherine keener (Q230378)
/m/02s2ft chris cooper (actor) (Q273208)
/m/09c7w0 usofa (Q30)
/m/0rs6x saint augustine (fl) (Q487988)
/m/079vf stanley lee (Q181900)
/m/0d90m x-men i (Q106182)
/m/015zyd los angeles conservatory of music (Q1026827)
/m/05vsxz chiwetel ejiofor (Q317343)
/m/06qgvf carla gugino (Q228871)
/m/04ljl_l golden raspberry award for worst supporting actor - motion picture (Q641316)
/m/03qcfvw g.i. joe: rise of cobra (film) (Q288645)
/m/0grwj oprah winfery (Q55800)
/m/05sxg2 theatre producer (Q1759246)
/m/0rh6k district of columbia county, dc (Q61)
/m/01lp8 kristendom (Q5043)
/m/05d7rk saeed jaffery (Q979990)
/m/027dtxw bafta award for best supporting actor 2010-2034 (Q548389)
/m/05hs4r heartland rock (Q747717)
/m/01pbxb bob seger & the silver bullet band (Q364131)
/m/03qcq hunter s thomson (Q219780)
/m/084w8 faulkner william (Q38392)
/m/01lxd4 avant-garde jazz (Q790188)
/m/0f0y8 jowcol music (Q7346)
/m/08815 yale model united nations (Q49112)
/m/05bnp0 james franco (Q306403)
/m/0g56t9t frankenweenie (full-length) (Q1051023)
/m/0160w greater bahamas (Q778)
/m/02vk52z world bank publications (Q7164)
/m/0dbpyd burnett, alan (Q360036)
/m/053y0s boat drunks (Q862359)
/m/0dz3r list of record producers (Q183945)
/m/016qtt bill ray cyrus (Q231259)
/m/08mbj5d /m/08mbj5d
/m/09sh8k fantastic four (2007 film) (Q390063)
/m/09zzb8 make-up artist (Q935666)
/m/05pd94v 2010 grammies (Q1349100)
/m/01vvydl dr.dre (Q6078)
/m/0gtsx8c american pie 4 (Q303040)
/m/0jgd argentinia (Q414)
/m/02r0csl bafta award for best makeup (Q918617)
/m/0m313 viola de lesseps (Q182944)
/m/02y_lrp love guru (Q656664)
/m/012d40 yuan-lou chen (Q36970)
/m/034qmv around the world in 80 days (2004 film) (Q156519)
/m/07rlg triathlon (Q10980)
/m/0b90_r republica de méxico (Q96)
/m/0g22z wall street 1 (Q332348)
/m/05b4l5x golden raspberry award for worst supporting actress (Q977915)
/m/018js4 lieutenant pete mitchell (Q110138)
/m/07fq1y rosemary ann harris (Q234195)
/m/02qgqt phillip seymour hofman (Q180560)
/m/028q6 dave brubeck (Q108597)
/m/02581q /m/02581q
/m/0sxg4 the dresser (1983 film) (Q620731)
/m/04xvlr costume drama (Q7168625)
/m/0fvf9q scott rudin productions (Q461529)
/m/0l6qt saga louts (Q654730)
/m/0jz9f miriam weinstein (Q465449)
/m/083shs the cider house rules (film) (film) (Q648977)
/m/0njvn berrien county, michigan (Q167656)
/m/09nqf currency of the united states (Q4917)
/m/0d1tm three-day event (Q838781)
/m/0154j belgium (Q31)
/m/01br2w making of the passion of the christ (Q51668)
/m/03rl1g thirty-second congress (Q4635114)
/m/05kkh geography of ohio (Q1397)
/m/0fbq2n /m/0fbq2n
/m/01r3hr scat back (Q912985)
/m/060bp list of prime ministers and premiers (Q14212)
/m/027nb iso 3166-1:dm (Q784)
/m/04qvl7 roger deakins (Q460277)
/m/02vxq9m /m/02vxq9m
/m/04yywz burt young (Q329700)
/m/0140g4 george washington duke (rocky v) (Q82949)
/m/01k7d9 h.r. walston (Q692800)
/m/0k049 history of beverly hills, california (Q127856)
/m/02p65p donald frank "don" cheadle (Q272019)
/m/0337vz larenz tate (Q1805906)
/m/06688p john barrowman (Q24632)
/m/0b2v79 the untouchables (film) (Q108525)
/m/01c0cc technische hochschule berlin-charlottenburg (Q51985)
/m/01jc6q cabaret (1972 movie) (Q918363)
/m/040njc bafta award for best film (Q139184)
/m/0520r2x richard day (art director) (Q1247808)
/m/0cb77r little, thomas (Q505353)
/m/06j0md charles/burrows/charles productions (Q3050241)
/m/01453 a.c.f. fiorentina (Q2052)
/m/0dgrmp /m/0dgrmp
/m/028_yv c'era una volta in america (Q206388)
/m/03rjj 🇮🇹 (Q38)
/m/01914 parks in beijing (Q956)
/m/05_6_y david healy's international goals and caps (Q313617)
/m/02b15h doncaster rovers fc (Q19589)
/m/03c7ln jay bennett (Q5570613)
/m/026t6 sound of a drum (Q11404)
/m/02pprs alto sax (Q445275)
/m/07s3vqk my world (ray charles album) (Q544387)
/m/05zjtn4 north texas state college (Q860527)
/m/0197tq geetali norah jones shankar (Q549981)
/m/02_fm2 morph (disney character) (Q392530)
/m/0411q 1 bourbon, 1 scotch, 1 beer (Q44570)
/m/016z4k singer songwriters (Q488205)
/m/06151l freddy rodríguez (actor) (Q362697)
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/m/03j7cf trabzonspor a.ş. (Q192641)
/m/04ls81 /m/04ls81
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/m/0t_gg un/locode:usmqi (Q49143)
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/m/02xs6_ hannibal (movie) (Q722682)
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/m/01fmz6 k.c. & the sunshine band (Q1130581)
/m/01fwf1 susanna york (Q225509)
/m/03cx282 philippe rousselot (Q750718)
/m/0dzlbx iron man (2008 movie) (Q192724)
/m/015t7v bernard hill (Q310515)
/m/0c3jz thora birch (Q236875)
/m/09q23x charlie wilson's war (film) (Q273568)
/m/03tn80 congo (movie) (Q598860)
/m/071fb mgao dialect (Q7838)
/m/081k8 william shake-speare (Q692)
/m/01gstn 16th united states congress (Q4552253)
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/m/011ycb quiz show (movie) (Q651722)
/m/0bqytm ballhaus, michael (Q62547)
/m/0tln7 history of natchitoches, louisiana (Q2278195)
/m/062zm5h the avengers (2012 movie) (Q182218)
/m/04nm0n0 good (film) (Q2601102)
/m/0b6jkkg filmfareawardbestfilm 2011-2030 (Q1414525)
/m/04fcx7 adam mckay (Q350405)
/m/0421ng bullets over broadway (Q1004531)
/m/04fcjt universal records (defunct record label) (Q2482872)
/m/0278x6s geraghty, brian (Q460563)
/m/03359d ali larter (Q201994)
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/m/02ll45 the mission (1986 film) (Q302490)
/m/0191n blue velvet in popular culture (Q660950)
/m/069nzr kyle chandler (Q359604)
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/m/06y57 sydneian (Q3130)
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/m/048q6x robert xavier morse (Q218122)
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gitextract_671zkorr/
├── .gitignore
├── LICENSE
├── README.md
├── config/
│ ├── homogeneous_graph/
│ │ ├── citeseer.yaml
│ │ ├── cora.yaml
│ │ └── pubmed.yaml
│ ├── inductive/
│ │ ├── fb15k237.yaml
│ │ └── wn18rr.yaml
│ └── knowledge_graph/
│ ├── fb15k237.yaml
│ ├── fb15k237_visualize.yaml
│ ├── ogbl-biokg.yaml
│ ├── ogbl-biokg_test.yaml
│ └── wn18rr.yaml
├── data/
│ └── fb15k237_entity.txt
├── nbfnet/
│ ├── __init__.py
│ ├── dataset.py
│ ├── layer.py
│ ├── model.py
│ ├── task.py
│ └── util.py
├── requirements.txt
└── script/
├── run.py
└── visualize.py
SYMBOL INDEX (78 symbols across 7 files)
FILE: nbfnet/dataset.py
class InductiveKnowledgeGraphDataset (line 14) | class InductiveKnowledgeGraphDataset(data.KnowledgeGraphDataset):
method load_inductive_tsvs (line 16) | def load_inductive_tsvs(self, train_files, test_files, verbose=0):
method __getitem__ (line 86) | def __getitem__(self, index):
method split (line 89) | def split(self):
class CoraLinkPrediction (line 100) | class CoraLinkPrediction(datasets.Cora):
method __init__ (line 102) | def __init__(self, **kwargs):
method __getitem__ (line 106) | def __getitem__(self, index):
method __len__ (line 109) | def __len__(self):
method split (line 112) | def split(self, ratios=(85, 5, 10)):
class CiteSeerLinkPrediction (line 124) | class CiteSeerLinkPrediction(datasets.CiteSeer):
method __init__ (line 126) | def __init__(self, **kwargs):
method __getitem__ (line 130) | def __getitem__(self, index):
method __len__ (line 133) | def __len__(self):
method split (line 136) | def split(self, ratios=(85, 5, 10)):
class PubMedLinkPrediction (line 148) | class PubMedLinkPrediction(datasets.PubMed):
method __init__ (line 150) | def __init__(self, **kwargs):
method __getitem__ (line 154) | def __getitem__(self, index):
method __len__ (line 157) | def __len__(self):
method split (line 160) | def split(self, ratios=(85, 5, 10)):
class FB15k237Inductive (line 172) | class FB15k237Inductive(InductiveKnowledgeGraphDataset):
method __init__ (line 184) | def __init__(self, path, version="v1", verbose=1):
class WN18RRInductive (line 211) | class WN18RRInductive(InductiveKnowledgeGraphDataset):
method __init__ (line 223) | def __init__(self, path, version="v1", verbose=1):
class OGBLBioKG (line 250) | class OGBLBioKG(data.KnowledgeGraphDataset):
method __init__ (line 252) | def __init__(self, path, verbose=1):
method load_ogb (line 259) | def load_ogb(self, dataset, verbose=1):
method split (line 333) | def split(self, test_negative=True):
FILE: nbfnet/layer.py
class GeneralizedRelationalConv (line 11) | class GeneralizedRelationalConv(layers.MessagePassingBase):
method __init__ (line 20) | def __init__(self, input_dim, output_dim, num_relation, query_input_di...
method message (line 49) | def message(self, graph, input):
method aggregate (line 78) | def aggregate(self, graph, message):
method message_and_aggregate (line 108) | def message_and_aggregate(self, graph, input):
method combine (line 160) | def combine(self, input, update):
FILE: nbfnet/model.py
class NeuralBellmanFordNetwork (line 17) | class NeuralBellmanFordNetwork(nn.Module, core.Configurable):
method __init__ (line 19) | def __init__(self, input_dim, hidden_dims, num_relation=None, symmetri...
method remove_easy_edges (line 51) | def remove_easy_edges(self, graph, h_index, t_index, r_index=None):
method negative_sample_to_tail (line 70) | def negative_sample_to_tail(self, h_index, t_index, r_index):
method as_relational_graph (line 79) | def as_relational_graph(self, graph, self_loop=True):
method bellmanford (line 95) | def bellmanford(self, graph, h_index, r_index, separate_grad=False):
method forward (line 132) | def forward(self, graph, h_index, t_index, r_index=None, all_loss=None...
method visualize (line 164) | def visualize(self, graph, h_index, t_index, r_index):
method beam_search_distance (line 187) | def beam_search_distance(self, graphs, h_index, t_index, num_beam=10):
method topk_average_length (line 242) | def topk_average_length(self, distances, back_edges, t_index, k=10):
FILE: nbfnet/task.py
class KnowledgeGraphCompletionExt (line 20) | class KnowledgeGraphCompletionExt(tasks.KnowledgeGraphCompletion, core.C...
method __init__ (line 22) | def __init__(self, model, criterion="bce",
method preprocess (line 30) | def preprocess(self, train_set, valid_set, test_set):
method target (line 54) | def target(self, batch):
method evaluate (line 60) | def evaluate(self, pred, target):
class LinkPrediction (line 104) | class LinkPrediction(tasks.Task, core.Configurable):
method __init__ (line 108) | def __init__(self, model, criterion="bce", metric=("auroc", "ap"), num...
method preprocess (line 116) | def preprocess(self, train_set, valid_set, test_set):
method forward (line 130) | def forward(self, batch):
method _strict_negative (line 154) | def _strict_negative(self, count, split="train"):
method predict_and_target (line 176) | def predict_and_target(self, batch, all_loss=None, metric=None):
method evaluate (line 200) | def evaluate(self, pred, target):
class InductiveKnowledgeGraphCompletion (line 220) | class InductiveKnowledgeGraphCompletion(tasks.KnowledgeGraphCompletion, ...
method __init__ (line 222) | def __init__(self, model, criterion="bce", metric=("mr", "mrr", "hits@...
method preprocess (line 229) | def preprocess(self, train_set, valid_set, test_set):
method predict (line 253) | def predict(self, batch, all_loss=None, metric=None):
method target (line 294) | def target(self, batch):
method evaluate (line 321) | def evaluate(self, pred, target):
class KnowledgeGraphCompletionOGB (line 358) | class KnowledgeGraphCompletionOGB(tasks.KnowledgeGraphCompletion, core.C...
method __init__ (line 360) | def __init__(self, model, criterion="bce", evaluator=None, num_negativ...
method preprocess (line 369) | def preprocess(self, train_set, valid_set, test_set):
method _strict_negative (line 399) | def _strict_negative(self, pos_h_index, pos_t_index, pos_r_index):
method predict (line 438) | def predict(self, batch, all_loss=None, metric=None):
method target (line 466) | def target(self, batch):
method evaluate (line 473) | def evaluate(self, pred, target):
FILE: nbfnet/util.py
function get_root_logger (line 22) | def get_root_logger(file=True):
function create_working_directory (line 35) | def create_working_directory(cfg):
function detect_variables (line 62) | def detect_variables(cfg_file):
function load_config (line 71) | def load_config(cfg_file, context=None):
function parse_args (line 81) | def parse_args():
function build_solver (line 98) | def build_solver(cfg, dataset):
FILE: script/run.py
function train_and_validate (line 15) | def train_and_validate(cfg, solver):
function test (line 40) | def test(cfg, solver):
FILE: script/visualize.py
function load_vocab (line 18) | def load_vocab(dataset):
function visualize_path (line 31) | def visualize_path(solver, triplet, entity_vocab, relation_vocab):
Condensed preview — 23 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (669K chars).
[
{
"path": ".gitignore",
"chars": 83,
"preview": "# PyCharm\n/.idea\n\n# VS Code\n/.vscode\n\n# Python\n__pycache__\n*.pyc\n\n# macOS\n.DS_Store"
},
{
"path": "LICENSE",
"chars": 1065,
"preview": "MIT License\n\nCopyright (c) 2021 MilaGraph\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\n"
},
{
"path": "README.md",
"chars": 5907,
"preview": "# NBFNet: Neural Bellman-Ford Networks #\n\nThis is the official codebase of the paper\n\n[Neural Bellman-Ford Networks: A G"
},
{
"path": "config/homogeneous_graph/citeseer.yaml",
"chars": 557,
"preview": "output_dir: ~/experiments/\n\ndataset:\n class: CiteSeerLinkPrediction\n path: ~/datasets/homogeneous_graphs/\n\ntask:\n cla"
},
{
"path": "config/homogeneous_graph/cora.yaml",
"chars": 553,
"preview": "output_dir: ~/experiments/\n\ndataset:\n class: CoraLinkPrediction\n path: ~/datasets/homogeneous_graphs/\n\ntask:\n class: "
},
{
"path": "config/homogeneous_graph/pubmed.yaml",
"chars": 555,
"preview": "output_dir: ~/experiments/\n\ndataset:\n class: PubMedLinkPrediction\n path: ~/datasets/homogeneous_graphs/\n\ntask:\n class"
},
{
"path": "config/inductive/fb15k237.yaml",
"chars": 649,
"preview": "output_dir: ~/experiments/\n\ndataset:\n class: FB15k237Inductive\n path: ~/datasets/knowledge_graphs/\n version: {{ versi"
},
{
"path": "config/inductive/wn18rr.yaml",
"chars": 620,
"preview": "output_dir: ~/experiments/\n\ndataset:\n class: WN18RRInductive\n path: ~/datasets/knowledge_graphs/\n version: {{ version"
},
{
"path": "config/knowledge_graph/fb15k237.yaml",
"chars": 606,
"preview": "output_dir: ~/experiments/\n\ndataset:\n class: FB15k237\n path: ~/datasets/knowledge_graphs/\n\ntask:\n class: KnowledgeGra"
},
{
"path": "config/knowledge_graph/fb15k237_visualize.yaml",
"chars": 605,
"preview": "output_dir: ~/experiments/\n\ndataset:\n class: FB15k237\n path: ~/datasets/knowledge_graphs/\n\ntask:\n class: KnowledgeGra"
},
{
"path": "config/knowledge_graph/ogbl-biokg.yaml",
"chars": 729,
"preview": "output_dir: ~/experiments/\n\ndataset:\n class: OGBLBioKG\n path: ~/datasets/knowledge_graphs/\n\ntask:\n class: KnowledgeGr"
},
{
"path": "config/knowledge_graph/ogbl-biokg_test.yaml",
"chars": 742,
"preview": "output_dir: ~/experiments/\n\ndataset:\n class: OGBLBioKG\n path: ~/datasets/knowledge_graphs/\n\ntask:\n class: KnowledgeGr"
},
{
"path": "config/knowledge_graph/wn18rr.yaml",
"chars": 577,
"preview": "output_dir: ~/experiments/\n\ndataset:\n class: WN18RR\n path: ~/datasets/knowledge_graphs/\n\ntask:\n class: KnowledgeGraph"
},
{
"path": "data/fb15k237_entity.txt",
"chars": 556731,
"preview": "/m/027rn\tlanguages of dominican republic (Q786)\n/m/06cx9\trepublic (government) (Q7270)\n/m/017dcd\trangers in reverse (Q51"
},
{
"path": "nbfnet/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "nbfnet/dataset.py",
"chars": 14201,
"preview": "import os\nimport csv\nimport glob\nfrom tqdm import tqdm\nfrom ogb import linkproppred\n\nimport torch\nfrom torch.utils impor"
},
{
"path": "nbfnet/layer.py",
"chars": 7872,
"preview": "import torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom torch_scatter import scatter_add, scatter_m"
},
{
"path": "nbfnet/model.py",
"chars": 11923,
"preview": "from collections.abc import Sequence\n\nimport torch\nfrom torch import nn\nfrom torch import autograd\n\nfrom torch_scatter i"
},
{
"path": "nbfnet/task.py",
"chars": 22335,
"preview": "import math\n\nimport torch\nfrom torch.nn import functional as F\nfrom torch.utils import data as torch_data\n\nfrom ogb impo"
},
{
"path": "nbfnet/util.py",
"chars": 3838,
"preview": "import os\nimport time\nimport logging\nimport argparse\n\nimport yaml\nimport jinja2\nfrom jinja2 import meta\nimport easydict\n"
},
{
"path": "requirements.txt",
"chars": 77,
"preview": "torch>=1.8.0\ntorch-scatter>=2.0.8\ntorchdrug>=0.1.2\nogb>=1.3.0\neasydict\npyyaml"
},
{
"path": "script/run.py",
"chars": 1690,
"preview": "import os\nimport sys\nimport math\nimport pprint\n\nimport torch\n\nfrom torchdrug import core\nfrom torchdrug.utils import com"
},
{
"path": "script/visualize.py",
"chars": 3106,
"preview": "import os\nimport sys\nimport pprint\n\nimport torch\n\nfrom torchdrug import core\nfrom torchdrug.utils import comm\n\nsys.path."
}
]
About this extraction
This page contains the full source code of the DeepGraphLearning/NBFNet GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 23 files (620.1 KB), approximately 256.9k tokens, and a symbol index with 78 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.